1. Technical Field
The present disclosure relates generally to processing of radio frequency identification (RFID) events, and, more particularly, to a method and an apparatus for processing complex RFID events.
2. Discussion of the Related Art
RFID technology uses radio-frequency waves to transfer data between readers and moveable tagged objects. Thus it is possible to create a physically linked world in which every object can be numbered, identified, catalogued, and tracked. RFID is automatic and fast, and does not require line of sight or contact between readers and tagged objects With such significant technology advantages, RFID has been gradually adopted and deployed in a wide area of applications, such as access control, library checkin and checkout, document tracking, smart box, highway tolls, supply chain and logistics, security, and healthcare.
RFID technology enables the identification and tracking of physical objects though an Electronic Product Code (EPC) tag. Electronic Product Codes are part of an identification scheme for universally identifying physical objects and are defined by EPCGlobal. By tagging objects with an EPC, the identifications and behaviors of these objects can be precisely observed and tracked.
A simple RFID system is illustrated in FIG. 1. The RFID system consists of a host computer 110, an RFID reader 120, an antenna 130 (often combined with a reader), and an RFID tag 140.
The RFID tag 140 can be read-only or read-write, and the RFID tag's 140 memory can be factory or field programmed. The RFID tag 140 is uniquely identified by an EPC stored in its memory. EPCglobal classifies RFID tags into five classes as shown in Table 1. Class 0 and 1 are read-only; Class 2 tags are read-write by readers; Class 3 tags come with onboard sensors that can write data into the tags; Class 4 tags are actually wireless sensors used for sensor networks; and Class 5 tags are readers.
TABLE 1EPC Classes of RFID tags.ClassDescription0Read-only. Factory Programmed ID.1Write once read only. Factory or user programmed ID.2Read-write. Users can read and write to tag's memory froma writer (also a reader).3Read-write with onboard sensors. Sensors write measurementto tags' memory.4Read-write with integrated transmitters. Active tags thatcan communicate with other class 4 tags or readers.5Readers. Wireless networked readers that can communicatewith each other.
The RFID reader 120 sends energy through radio frequency (RF) signal to the RFID tag 140 for power, and the RFID tag 140 sends back a modulated signal with data with includes the EPC and possibly some additional data. The RFID reader 120 then decodes and sends the data to the host computer 110.
RFID readers can be mounted at a fixed point such as a warehouse entrance/exit or point of sale. RFID readers can be deployed at different locations and networked together, which provides an RFID-based pervasive computing environment. This is illustrated in FIG. 2, where L1-L6 denote different locations mounted with readers. Tagged objects moving in this environment will then be automatically sensed and observed with their identifications, locations and movement paths. RFID readers can also be mobile: tethered, hand-held, or wireless.
Data recovered by RFID Readers from RFID tags are also known as observations. Such observations are raw data and provide no explicit semantic meanings. They have to be transformed into semantic data properly represented with their own data models before they can be integrated into applications.
There are generally two types of RFID applications: i) history oriented object tracking and ii) real-time oriented monitoring. Both application types need to transform RFID observations into logic data. In history oriented tracking, RFID data streams are collected from multiple RFID readers at distributed locations, and transformed into semantic data stored in an RFID data store. The semantics of the data include location, aggregation, and temporal.
Location can be a geographic location or a symbolic location such as a warehouse, a shipping route, a surgery room, or a smart box. A change of location of an EPC-tagged object can be signaled by certain RFID readers. The location histories of RFID objects are then transformed automatically from these RFID readings, and stored in a location history relation in an RFID data store.
Aggregation is the formation of hierarchical relationships among objects. A common case is the containment relationship, illustrated in FIG. 3. Referring to FIG. 3, on a packing conveyer, a sequence of tagged items move through Reader A and are observed by the readers as a sequence of observations. Then a tagged case is read by Reader B as another observation. After that, all the items of this sequence are packed into a case. An aggregation relationship often implies important semantics. For example, a case with its contained items shares the same locations. Another similar concept is association, where tagged objects are associated with certain relationships. For example, a laptop may be associated with a list of authorized users; and a surgery kit may include a list of tools belonging to the kit.
RFID observations and their collected data are highly temporal-oriented. All events (e.g., observations) are associated with timestamps when the event happens. Observations can represent different semantics, including (1) location changes, (2) aggregation/association relationship changes, (3) start (or end) of operations/processes; and (4) occurrences of new events. Thus, a history oriented data model is essential to preserve the event and state-change histories, which are the key to tracking, tracing, and monitoring physical objects in a pervasive computing environment.
RFID is also widely used for real-time applications, where patterns of RFID observations implying special application logic can trigger real-time responses. For example, a company could use RFID tags to identify asset items and employees in the building, and only allow authorized users to move an asset item out of the building. When an unauthorized employee or a criminal takes a laptop with an embedded RFID tag out of the building, the system sends an alert to security personnel for response.
RFID events however, have their own characteristics and cannot be easily supported by traditional event systems. RFID events are temporally constrained Both the temporal distance between two events and the interval of a single event are critical for event detection. Such temporal constraints, however, are not well supported by traditional Event-Condition-Action (ECA) rules detection systems. In addition, non-spontaneous events, including negative events and temporal constrained events, are important for many RFID applications but difficult to support in past event detection engines. The actions from RFID events are quite different as well. They neither trigger new primitive events for the system, nor lead to a cascade of rule firings as in active databases. Thus, there is an opportunity to build a scalable rule-based system to process complex RFID events.